Introduction GNS data to microbe classification

In this notebook we will analyse the data from Bacterial Identification Dataset (click on it to visit the siteweb of this dataset). We will first do the analysis of the dataset, with the EDA part, and after that do Feature Selection, to finally construct models of classification of the bacterias based on our features.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import plotly.express as px

0. Importing Dataset

path_train = './data/train.csv'
df = pd.read_csv(path_train, sep  = ',', index_col = 0)

Y = df['target']
X = df.drop(columns = ['target'])

from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
Y = label_encoder.fit_transform(df['target'])

1. Basic analyses of the Dataset

X.info()

<class 'pandas.core.frame.DataFrame'>
Index: 200000 entries, 0 to 199999
Columns: 286 entries, A0T0G0C10 to A10T0G0C0
dtypes: float64(286)
memory usage: 437.9 MB

print(f"The dimensions of my entire DataFrame are: {X.shape}")
print(f"The dimension of my vector target is: {Y.shape} \n")

nul = X.isnull().sum()[X.isnull().sum() != 0]
non_numeric = X.dtypes[X.dtypes != 'float64']

print(f"The DataFrame has {len(nul)} nul values")
print(f"The DataFrame has {len(non_numeric)} non numeric columns \n")

duplicated = X.duplicated()

print(f"The DataFrame has {duplicated.sum()} duplicated values")

The dimensions of my entire DataFrame are: (200000, 286)
The dimension of my vector target is: (200000,) 

The DataFrame has 0 nul values
The DataFrame has 0 non numeric columns 

The DataFrame has 76007 duplicated values

These 76087 duplicated rows can be a problem because they repeat the data in a specific class, making an influence in the final prediction.

## Droping the duplicates
X   = X.drop_duplicates()
dfd = df.drop_duplicates()
Y   = Y[~duplicated]

X.info()

<class 'pandas.core.frame.DataFrame'>
Index: 123993 entries, 0 to 199997
Columns: 286 entries, A0T0G0C10 to A10T0G0C0
dtypes: float64(286)
memory usage: 271.5 MB

So, with this simple cleaning we reduced a considerable number of memory usage in this dataset (~166MB), and we have now 123993 entries, which is agreeing with the 76087 rows we droped.

## Verifying the class-balanced of the samples
classes = dfd['target'].value_counts().reset_index()
classes.columns = ['species', 'quantity']
classes['percentage'] = classes['quantity']*100 / classes['quantity'].sum()
classes

	species	quantity	percentage
0	Bacteroides_fragilis	12522	10.098957
1	Campylobacter_jejuni	12469	10.056213
2	Klebsiella_pneumoniae	12420	10.016694
3	Streptococcus_pneumoniae	12416	10.013469
4	Staphylococcus_aureus	12415	10.012662
5	Streptococcus_pyogenes	12406	10.005404
6	Salmonella_enterica	12390	9.992500
7	Enterococcus_hirae	12373	9.978789
8	Escherichia_coli	12297	9.917495
9	Escherichia_fergusonii	12285	9.907817

2. Exploratory Data Analysis

Now we need to verify the balance of the classes, and verify the basic statistics of the datasaet, correlation matrix and heatmaps.

## Colorbar to verify the balance of classes
colors = sns.color_palette("Spectral", 9)
plt.pie(classes['quantity'],labels= classes['species'], colors=colors, startangle=90, autopct='%1.1f%%')
plt.show()

corr = X.corr()
corr.style.background_gradient(cmap='YlOrRd')

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A4T1G3C2	0.002019	-0.006769	-0.000200	0.010565	0.003662	0.002857	0.023551	-0.002832	-0.008737	-0.016064	-0.036984	-0.011125	-0.045075	0.001390	0.009144	0.008104	0.043782	0.023311	0.017649	-0.025591	-0.087029	-0.025914	-0.001045	-0.014027	0.050155	0.058420	0.026578	0.029200	-0.014946	0.017218	-0.012674	-0.054682	0.011093	0.067866	0.049680	0.028436	-0.006547	-0.018075	0.007077	-0.043434	-0.030321	-0.031105	0.018482	-0.031807	-0.059830	-0.050071	-0.030453	-0.055253	-0.063761	-0.067161	-0.077061	-0.063024	-0.094118	-0.135037	-0.127658	-0.077029	-0.108324	-0.183683	-0.163738	-0.129136	-0.141001	-0.169889	-0.116580	-0.107732	-0.063651	-0.003968	0.000349	0.074534	0.006378	0.033781	0.020889	0.026427	0.045579	0.008759	-0.019716	-0.109390	-0.023551	-0.010698	0.012605	0.064232	0.028699	0.049075	0.067602	-0.032604	-0.039488	-0.031672	-0.049142	0.025026	0.069266	0.040818	0.021274	0.209909	0.201330	-0.014544	-0.057487	0.096998	0.055047	0.114929	-0.053309	-0.071308	-0.109280	-0.003833	-0.125665	-0.088645	-0.030953	0.117189	-0.124856	-0.185843	-0.260148	-0.241629	-0.190151	-0.214121	-0.174759	-0.166075	-0.191433	-0.224588	-0.256986	-0.188550	-0.191928	-0.187395	-0.093061	-0.036891	0.006364	0.018958	0.047718	0.036362	0.042880	0.008508	-0.004829	-0.000206	-0.051442	-0.007819	0.032730	0.032319	0.134825	0.031634	-0.060277	-0.060869	0.296652	-0.054239	0.242823	0.068576	0.292515	0.257674	-0.056470	-0.072102	-0.160254	-0.037549	0.028501	-0.000311	-0.134561	-0.196525	-0.182748	-0.102641	0.102498	0.056225	-0.298469	-0.375899	-0.374814	-0.295295	-0.315610	-0.107175	0.100269	-0.291435	-0.286126	-0.224088	-0.014964	-0.014153	0.041871	0.466302	0.352438	0.437453	-0.024224	-0.007078	-0.053614	0.335692	-0.014790	0.456627	-0.020751	0.355366	-0.056579	-0.150783	0.110223	0.180989	-0.007196	0.096367	0.359039	-0.210904	0.211876	0.083038	-0.072292	-0.227653	-0.330784	-0.426708	0.107068	0.528336	-0.344126	-0.395375	-0.339850	-0.351148	-0.350174	-0.268232	-0.027287	0.681910	0.654474	0.630360	-0.005745	-0.033267	-0.004837	-0.123509	-0.166705	0.379922	1.000000	0.702185	-0.099484	-0.252467	-0.325447	0.354304	0.495538	-0.203353	-0.323483	-0.123451	0.363859	-0.319562	-0.382774	0.126941	-0.372407	-0.352019	-0.365213	-0.289639	-0.067914	-0.062510	0.600647	-0.065251	-0.004792	-0.009239	-0.168162	-0.279336	-0.247080	0.328212	-0.111391	-0.287525	-0.385508	-0.016234	0.289469	0.120776	-0.093868	-0.340907	0.114185	-0.344533	0.329350	-0.079486	-0.127414	-0.128093	-0.092125	-0.035107	-0.222597	-0.261057	-0.265460	-0.192286	-0.315662	-0.356678	-0.291748	-0.322933	-0.321918	-0.286291	-0.105234	-0.150482	-0.173159	-0.089057	-0.213474	-0.255105	-0.205617	-0.286236	-0.288385	-0.264480	-0.116339	-0.177945	-0.106188	-0.200524	-0.209093	-0.191015	-0.072193	-0.069074	-0.058905	-0.023503
A4T1G4C1	-0.004319	-0.022945	-0.010374	-0.060588	-0.070910	-0.075437	-0.033020	-0.084011	-0.033802	-0.011750	-0.017346	0.048459	-0.117736	-0.062468	-0.109655	-0.092836	-0.059902	-0.090242	-0.062570	-0.092752	-0.077563	-0.054876	-0.083879	-0.112441	-0.079063	-0.070454	-0.110703	-0.122455	-0.082796	-0.005618	-0.047947	-0.134384	-0.153379	-0.119719	-0.104348	-0.128537	-0.081361	-0.064271	-0.055286	-0.114680	-0.172832	-0.174622	-0.139889	-0.133396	-0.088302	-0.063069	-0.038847	-0.153273	-0.151555	-0.123593	-0.073009	-0.070464	-0.123620	-0.151184	-0.119800	-0.065042	-0.063221	-0.142516	-0.121940	-0.102977	-0.088508	-0.104977	-0.080194	-0.054252	-0.036621	0.008530	-0.023196	0.097746	-0.049950	-0.090532	-0.087854	-0.103950	-0.037374	-0.011332	-0.060034	-0.140166	-0.059386	-0.123202	-0.133733	-0.092287	-0.124485	-0.102309	-0.056667	-0.097931	-0.072630	-0.078094	-0.147193	-0.160177	-0.109702	-0.163478	-0.176121	0.267928	0.305634	-0.051260	-0.158866	-0.044894	-0.141345	-0.037002	-0.224276	-0.106405	-0.169267	-0.046876	-0.319098	-0.265570	-0.086227	0.192291	-0.108493	-0.215095	-0.332137	-0.277655	-0.181272	-0.167584	-0.067602	-0.038166	-0.150261	-0.132410	-0.166662	-0.097377	-0.126535	-0.102274	-0.067140	-0.051809	-0.045545	-0.105194	-0.096715	-0.120394	-0.088701	-0.046928	-0.083777	-0.024840	-0.110736	-0.125991	-0.165251	-0.175262	-0.008821	-0.153156	-0.165640	-0.088718	0.414236	-0.224106	0.186569	-0.133796	0.268633	0.282793	-0.146766	-0.104786	-0.335210	-0.221823	-0.096555	-0.088352	-0.216994	-0.224168	-0.191022	-0.083433	0.227948	0.196392	-0.227938	-0.322715	-0.320179	-0.224467	-0.205710	0.085480	0.298669	-0.164757	-0.163040	-0.132834	-0.056231	-0.124341	-0.103919	0.531568	0.332888	0.499171	-0.111756	-0.028938	-0.135848	0.345296	-0.237435	0.478736	-0.220837	0.394783	-0.107217	-0.201289	0.044137	0.083034	-0.162659	0.020067	0.498677	-0.209444	0.337382	0.148164	-0.059069	-0.214504	-0.293320	-0.383894	0.302679	0.745324	-0.221828	-0.279321	-0.233001	-0.221249	-0.213155	-0.142681	-0.070652	0.836729	0.732530	0.654753	-0.161164	-0.118000	0.016808	-0.159062	-0.298818	0.307188	0.702185	1.000000	-0.160202	-0.255039	-0.390307	0.464354	0.632700	-0.248274	-0.252889	0.015625	0.585667	-0.267515	-0.263186	0.358106	-0.277470	-0.219290	-0.225525	-0.167496	-0.090959	-0.111934	0.738861	-0.196381	0.000958	-0.054188	-0.176914	-0.297680	-0.306982	0.431588	-0.120347	-0.235689	-0.307154	0.135496	0.467265	0.330735	0.097218	-0.226083	0.331479	-0.196119	0.534919	-0.066977	-0.145328	-0.143056	-0.127216	-0.015635	-0.178215	-0.191404	-0.199527	-0.156105	-0.204885	-0.248473	-0.185529	-0.182354	-0.176414	-0.156404	-0.078061	-0.105506	-0.145303	-0.049673	-0.140353	-0.164212	-0.127282	-0.182333	-0.158051	-0.155798	-0.067831	-0.114338	-0.076979	-0.105064	-0.122970	-0.114306	-0.041376	-0.036551	-0.013130	-0.017583
A4T1G5C0	-0.007913	0.009183	0.045563	0.013596	0.017700	0.012566	-0.006700	-0.000189	0.006119	-0.007435	-0.005354	-0.109305	0.071570	0.034875	0.007196	0.014660	-0.055371	-0.007921	0.007526	0.032378	0.008833	0.038403	0.052230	0.005302	-0.015264	-0.047810	-0.028730	0.017013	0.036053	0.036194	0.067305	0.091893	0.054524	0.004961	-0.022476	0.000431	-0.019058	0.012878	0.043081	0.112822	0.116175	0.039809	0.057545	0.049975	0.034661	0.106933	0.016957	0.054552	0.037452	0.030726	0.045310	0.083113	0.029144	0.034291	-0.002433	0.003327	0.062722	0.050389	-0.003843	-0.002519	0.043951	-0.004916	0.058604	0.025183	-0.024092	0.055323	0.000532	-0.138607	-0.001116	-0.004774	-0.008421	0.000013	-0.039741	-0.065479	0.031400	0.082554	0.019831	0.058797	0.033288	-0.019225	0.011376	-0.015065	-0.058188	0.061409	0.040776	0.069060	0.064210	0.021725	0.026965	0.018918	0.045327	-0.126823	-0.096008	0.049396	0.076205	0.033543	0.039908	-0.009628	0.074351	0.032232	0.139420	0.052240	0.146488	0.087570	0.028487	-0.075270	0.088154	0.087745	0.136218	0.093353	0.085353	0.106937	0.032736	-0.045692	0.019498	0.039860	-0.011520	-0.010522	0.006008	-0.007987	0.000683	0.019633	-0.025675	0.024230	-0.029604	0.011881	0.007179	-0.052305	0.052766	0.016946	0.047130	0.016920	0.043630	0.035655	-0.033277	0.032012	0.064585	0.050742	-0.108199	0.109351	-0.083582	0.046395	-0.097462	-0.059092	0.109475	0.043977	0.185877	0.091867	0.081216	0.058542	0.124828	0.152448	0.113431	-0.002763	-0.121197	-0.056247	0.088118	0.080889	0.054495	0.061817	0.003630	-0.067574	-0.133969	-0.013441	-0.044192	-0.021697	0.032449	0.068030	-0.028326	-0.173253	-0.099251	-0.131371	0.095414	0.036521	0.087649	-0.072836	0.088009	-0.128868	0.071595	-0.080984	0.115543	0.119274	0.044300	0.017548	0.088188	0.104251	-0.102479	0.078852	-0.037899	0.002160	0.078472	0.077260	0.086194	0.096458	-0.083386	-0.197896	-0.009081	-0.010951	0.010702	-0.002275	-0.019772	-0.045632	0.030116	-0.189098	-0.145457	-0.109086	0.098099	0.106888	-0.045307	0.084041	0.123882	-0.011169	-0.099484	-0.160202	1.000000	0.108972	0.136439	-0.050703	-0.111060	0.136662	0.039452	0.027973	-0.127931	0.079444	0.011012	-0.131322	0.030869	-0.047456	-0.005599	-0.049629	0.028137	0.050969	-0.155401	0.119499	-0.037401	0.063175	0.057797	0.087097	0.151384	-0.012285	0.098022	0.017627	0.033966	-0.036694	-0.113802	-0.131810	-0.058414	0.013090	-0.159096	-0.054509	-0.166711	0.026047	0.044591	0.065507	0.092587	0.032881	0.010850	0.016952	0.047276	0.056763	0.010028	0.024732	0.016313	-0.036773	-0.054370	-0.023412	0.036620	0.044423	0.068594	0.044229	-0.007297	0.017325	-0.015067	0.001642	-0.030975	-0.035323	0.005414	0.006478	0.048757	-0.032808	0.001025	-0.033267	-0.019038	0.000552	-0.005318	0.013870
A4T2G0C4	0.027911	0.002451	-0.037894	-0.103791	-0.126064	-0.124711	-0.126934	-0.105008	-0.050436	-0.012235	0.004259	-0.096858	0.016460	-0.062139	-0.132749	-0.183197	-0.226539	-0.169754	-0.079293	0.030581	0.055683	0.012731	-0.018021	-0.120446	-0.199607	-0.248472	-0.197347	-0.136464	-0.039976	-0.016055	0.048848	-0.012919	-0.070463	-0.176635	-0.193593	-0.150080	-0.082693	0.028848	0.042388	0.041683	-0.036140	-0.099788	-0.069840	0.014729	0.042995	0.064022	-0.049486	0.016348	-0.004178	0.058037	0.060774	0.085591	0.071563	0.119405	0.079695	0.065046	0.108231	0.138005	0.104660	0.078128	0.107797	0.118136	0.066227	0.051037	0.074989	-0.007390	0.002219	-0.161825	-0.102431	-0.153225	-0.195658	-0.170800	-0.207520	-0.158952	0.008414	0.081716	0.000443	-0.011601	-0.122074	-0.235476	-0.205497	-0.236562	-0.235148	-0.019702	0.029196	0.047059	-0.047937	-0.152509	-0.200100	-0.194661	-0.164152	-0.230919	-0.169431	-0.037038	-0.047063	-0.150494	-0.156307	-0.195337	-0.004946	0.031802	0.114581	-0.025612	0.051700	0.040922	0.014851	-0.071450	0.133476	0.147444	0.204574	0.183396	0.192064	0.177683	0.117246	0.056548	0.177088	0.168299	0.185734	0.094930	0.144320	0.112861	0.040538	-0.001815	-0.103931	-0.131263	-0.211935	-0.197217	-0.206167	-0.159343	-0.007221	-0.005132	0.034805	-0.115641	-0.151790	-0.168263	-0.272023	-0.173355	-0.022470	0.043726	-0.198752	-0.029981	-0.293016	-0.157621	-0.280621	-0.213907	0.073886	0.036430	0.161349	-0.014398	-0.043060	-0.018503	0.149141	0.194349	0.186206	0.078189	-0.071639	-0.016031	0.253507	0.297295	0.292324	0.212040	0.227520	0.109041	-0.030943	0.164635	0.142917	0.121244	0.007323	-0.020917	-0.170312	-0.357124	-0.305546	-0.321545	-0.001327	0.017329	0.065061	-0.190409	-0.058352	-0.340732	-0.101337	-0.228936	0.017261	0.120246	-0.028274	-0.103054	-0.023322	-0.013992	-0.174242	0.192294	-0.013410	0.079593	0.198638	0.180027	0.291741	0.337666	0.106708	-0.170704	0.216180	0.269350	0.242412	0.201548	0.205401	0.112376	0.029548	-0.287247	-0.300574	-0.283152	-0.032571	0.020360	-0.060448	0.091977	0.133038	-0.129998	-0.252467	-0.255039	0.108972	1.000000	0.267788	-0.006802	-0.121397	0.228925	0.253583	0.223338	-0.038394	0.261602	0.245063	0.032331	0.272238	0.217872	0.206719	0.159566	0.050077	0.043592	-0.239073	0.050371	-0.076367	0.037740	0.156492	0.226074	0.207881	-0.027778	0.102935	0.186812	0.261694	0.114452	-0.067872	0.015994	0.144470	0.234879	0.009039	0.191811	-0.132226	0.067976	0.121921	0.123615	0.092807	0.040262	0.151591	0.197987	0.200063	0.186613	0.204705	0.245017	0.189507	0.193626	0.181435	0.156789	0.104521	0.105195	0.137933	0.096192	0.132053	0.193326	0.138643	0.160341	0.171521	0.137885	0.079036	0.131210	0.097197	0.088213	0.125474	0.098383	0.044705	0.056739	0.040004	0.021110
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A4T2G2C2	-0.018715	-0.027805	-0.050382	-0.166838	-0.241180	-0.238950	-0.216182	-0.176775	-0.095865	0.002526	-0.024186	0.051025	-0.099073	-0.162649	-0.276852	-0.317389	-0.295255	-0.288618	-0.163183	-0.073352	-0.060971	-0.056202	-0.101870	-0.200018	-0.305854	-0.307989	-0.304431	-0.252087	-0.093454	-0.031664	-0.041784	-0.141869	-0.272973	-0.279028	-0.276983	-0.257279	-0.136573	-0.045081	-0.025220	-0.112158	-0.201620	-0.194107	-0.179565	-0.111543	-0.057307	-0.008078	-0.021430	-0.084845	-0.106507	-0.059086	-0.030235	-0.009888	-0.010292	-0.004644	-0.017363	0.011373	0.012892	0.026801	0.029261	-0.008115	0.025649	0.014387	-0.010660	-0.000680	0.002680	0.024017	-0.029507	-0.023422	-0.181474	-0.286688	-0.308584	-0.316295	-0.239877	-0.102030	-0.060652	-0.064331	-0.039191	-0.170731	-0.287790	-0.345698	-0.349115	-0.334989	-0.259079	-0.154053	-0.065204	-0.061895	-0.172233	-0.301777	-0.336217	-0.345532	-0.329254	-0.014516	0.062064	-0.087927	-0.166903	-0.235887	-0.265951	-0.203172	-0.185651	-0.027292	-0.056806	-0.061919	-0.184501	-0.160046	-0.045125	0.040295	0.036419	-0.014417	-0.025210	-0.036952	-0.007655	-0.003372	0.057579	0.081100	0.025846	0.025384	0.015130	0.031106	0.009253	0.011356	-0.007882	-0.026043	-0.087974	-0.264957	-0.319858	-0.338352	-0.298732	-0.169899	-0.090983	-0.047045	-0.092465	-0.211798	-0.326495	-0.353354	-0.315336	-0.316447	-0.181542	-0.081875	0.120614	-0.223656	-0.133442	-0.273938	-0.075533	0.001306	-0.056796	-0.045220	-0.149438	-0.153819	-0.106992	-0.062551	-0.065012	-0.013375	0.013798	0.083486	0.147978	0.134548	0.012282	0.043272	0.043523	0.019073	0.019332	0.161372	0.215345	-0.012062	-0.017165	-0.020099	-0.058904	-0.124958	-0.228710	0.084133	-0.055010	0.090201	-0.130956	-0.010891	-0.077452	0.058093	-0.265476	0.052921	-0.283352	0.075271	-0.051932	-0.053479	0.012965	-0.013053	-0.102207	-0.000767	0.208118	0.003351	0.338524	0.292079	0.171232	-0.003828	0.018224	0.023191	0.390926	0.501635	0.031753	0.025965	0.021196	-0.030466	-0.005088	-0.029446	-0.054889	0.457609	0.348484	0.277718	-0.174319	-0.082974	0.037073	-0.044043	-0.123411	0.159342	0.354304	0.464354	-0.050703	-0.006802	-0.029037	1.000000	0.486638	0.013173	0.036174	0.247180	0.495403	0.019709	0.009773	0.343857	0.002710	-0.016990	-0.020047	-0.035634	0.008559	-0.055296	0.404354	-0.119640	-0.014017	0.011400	-0.018986	-0.005689	-0.015565	0.321200	0.007326	0.026058	0.053799	0.249224	0.323985	0.289413	0.215399	0.039672	0.247719	-0.008610	0.304550	0.024134	-0.024200	-0.008928	-0.023519	0.049070	0.022808	0.049525	0.032274	0.015291	0.029266	0.021404	0.019317	-0.002214	-0.004255	-0.022941	0.009335	0.024458	0.006062	0.047956	-0.007510	0.025448	-0.004187	-0.012398	0.010828	-0.040886	0.000882	-0.004396	0.008691	0.019803	-0.000662	-0.022670	0.010875	-0.021875	-0.011226	0.019816
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A9T0G0C1	0.002309	-0.009734	-0.017538	-0.044852	-0.057235	-0.056181	-0.052579	-0.050799	-0.023632	-0.002492	0.018506	-0.024902	-0.041282	-0.048086	-0.079054	-0.084632	-0.091798	-0.078909	-0.055639	-0.035467	0.021785	-0.017100	-0.039850	-0.092446	-0.097208	-0.112434	-0.115024	-0.075193	-0.047813	-0.010592	-0.024380	-0.057693	-0.077308	-0.113554	-0.110750	-0.090221	-0.066246	-0.017911	0.000025	-0.048533	-0.069418	-0.102027	-0.084789	-0.021666	-0.015522	0.009505	-0.024721	-0.019581	-0.027868	-0.027339	0.020613	0.036206	0.077459	0.033445	0.056535	0.012511	0.053449	0.112912	0.096496	0.047685	0.104386	0.073939	0.050210	0.050240	0.033879	0.000807	0.001446	-0.034996	-0.032004	-0.081761	-0.083497	-0.076025	-0.085387	-0.062830	-0.022985	-0.010189	-0.022890	-0.063777	-0.101063	-0.121615	-0.108290	-0.115359	-0.103567	-0.052986	-0.019896	-0.028713	-0.095289	-0.123270	-0.127756	-0.134798	-0.124056	-0.064014	-0.038595	-0.019520	-0.071378	-0.113785	-0.131167	-0.137308	-0.072764	-0.035943	-0.016682	-0.054994	-0.066203	-0.045217	-0.023868	-0.008283	0.010950	0.061838	0.040835	0.041098	0.042934	0.075768	0.109392	0.099624	0.091255	0.133202	0.140785	0.148192	0.131810	0.094829	0.055172	-0.017192	-0.053143	-0.074071	-0.093074	-0.108841	-0.103344	-0.092323	-0.044634	-0.019289	-0.033182	-0.082010	-0.120694	-0.112158	-0.149092	-0.127477	-0.050106	-0.032789	-0.050091	-0.088719	-0.139546	-0.150220	-0.138297	-0.082612	-0.039446	-0.028359	-0.056732	-0.121925	-0.121143	-0.089280	0.015027	0.060059	0.025187	0.015296	-0.001175	0.022143	0.131159	0.117782	0.132016	0.110773	0.130971	0.140868	0.117751	0.155990	0.127403	0.109720	-0.020041	-0.061109	-0.094790	-0.090230	-0.092386	-0.092817	-0.055885	-0.019931	0.001464	-0.077522	-0.115792	-0.115533	-0.106030	-0.057778	-0.026111	-0.007353	-0.087821	-0.120461	-0.103889	-0.074958	-0.029144	0.030846	0.030564	0.023738	0.026837	0.029400	0.119098	0.079341	0.105700	0.021324	0.124679	0.138965	0.137580	0.156476	0.125374	0.116463	-0.014490	-0.039074	-0.068509	-0.082680	-0.068751	-0.000808	-0.027904	0.047830	-0.016754	-0.104451	-0.072193	-0.041376	-0.019038	0.044705	0.031051	0.010875	0.002821	0.094253	0.099779	0.098858	0.068681	0.106548	0.133830	0.105147	0.138203	0.127973	0.146921	0.135779	0.002424	-0.003090	-0.018079	-0.010388	-0.017432	0.021265	0.040854	0.058597	0.042110	0.024303	0.049652	0.090996	0.095250	0.120700	0.081231	0.104251	0.136341	0.138374	0.094021	0.135964	0.057560	0.028669	0.064990	0.065609	0.043290	0.027826	0.062030	0.107057	0.131469	0.069996	0.113082	0.163670	0.159328	0.136154	0.125427	0.123127	0.040404	0.075657	0.098341	0.053508	0.094288	0.129639	0.119231	0.125040	0.123897	0.106858	0.113082	0.099841	0.068419	0.094005	0.136338	0.124379	1.000000	0.080848	0.054605	0.009158
A9T0G1C0	0.001992	-0.010317	-0.023809	-0.058410	-0.072991	-0.071271	-0.067111	-0.059750	-0.029833	-0.003086	0.021141	-0.028254	-0.044405	-0.059832	-0.098799	-0.108011	-0.116982	-0.086270	-0.069952	-0.039184	0.025862	-0.023469	-0.049436	-0.099061	-0.138170	-0.137121	-0.138777	-0.113268	-0.057189	-0.012133	-0.026366	-0.070502	-0.096851	-0.137573	-0.150473	-0.112928	-0.041210	-0.024195	-0.010534	-0.041618	-0.091175	-0.116013	-0.077966	-0.045056	0.019246	-0.005224	-0.000230	-0.008477	-0.015847	0.020255	0.030816	0.060392	0.057718	0.089222	0.026293	0.030470	0.078601	0.129985	0.131236	0.058188	0.084084	0.106907	0.062833	0.056261	0.043903	0.000462	0.000559	-0.044533	-0.050276	-0.102058	-0.107201	-0.102148	-0.103111	-0.076871	-0.022959	-0.012450	-0.024691	-0.058045	-0.121527	-0.143066	-0.137854	-0.139661	-0.130740	-0.066035	-0.026208	-0.032062	-0.100299	-0.166143	-0.174461	-0.167618	-0.161257	-0.083425	-0.030829	-0.018694	-0.091698	-0.150336	-0.181653	-0.163427	-0.115257	-0.048622	0.024942	-0.070886	-0.089124	-0.083971	-0.052558	-0.021612	0.064507	0.086630	0.064704	0.083854	0.043445	0.131199	0.143745	0.113344	0.138116	0.148103	0.164961	0.127496	0.142752	0.130449	0.083309	-0.016381	-0.068308	-0.108991	-0.128724	-0.126027	-0.123111	-0.111911	-0.029192	-0.020098	-0.024650	-0.081261	-0.154050	-0.170447	-0.178491	-0.160326	-0.098431	-0.037500	-0.043840	-0.097944	-0.169012	-0.185509	-0.166514	-0.108475	-0.043496	-0.016970	-0.050710	-0.142888	-0.143784	-0.102809	-0.005939	0.066912	0.075332	0.001380	0.007087	0.016998	0.121550	0.136762	0.129882	0.108953	0.194078	0.188111	0.101733	0.180643	0.159963	0.120372	-0.022205	-0.074462	-0.123346	-0.104273	-0.143484	-0.103820	-0.066016	-0.022531	-0.029070	-0.104625	-0.148164	-0.126182	-0.154451	-0.095962	-0.020028	0.000541	-0.069268	-0.156459	-0.141483	-0.070884	-0.022494	0.067276	0.049293	0.006350	0.058542	0.045313	0.115334	0.127823	0.119931	0.035635	0.148309	0.158155	0.184733	0.180620	0.151209	0.148113	-0.020899	-0.034180	-0.078821	-0.101544	-0.074091	-0.039595	-0.033916	-0.001547	-0.032141	-0.118211	-0.069074	-0.036551	0.000552	0.056739	0.069487	-0.021875	0.026758	0.062862	0.116260	0.138832	0.083775	0.137358	0.161604	0.117266	0.155982	0.173102	0.146329	0.139368	0.019949	-0.005501	-0.006661	-0.030454	0.000649	0.018917	0.079545	0.032779	0.057946	0.044768	0.046961	0.109610	0.123055	0.171966	0.111963	0.125157	0.167033	0.166548	0.124220	0.164394	0.095749	0.033125	0.041695	0.061931	0.072194	0.018134	0.079810	0.118272	0.128184	0.104640	0.126569	0.194386	0.173397	0.159077	0.164430	0.137710	0.040315	0.129514	0.145953	0.074910	0.145061	0.189084	0.175300	0.209560	0.168282	0.196678	0.050405	0.129428	0.097321	0.132097	0.099614	0.109158	0.080848	1.000000	0.062229	0.028277
A9T1G0C0	0.003948	-0.010409	-0.021060	-0.019866	-0.044565	-0.048300	-0.041223	-0.051545	-0.026228	-0.001911	0.017487	-0.025792	-0.046257	-0.054906	-0.076185	-0.077379	-0.091154	-0.077563	-0.060064	-0.039472	0.027338	-0.023499	-0.051051	-0.105782	-0.091022	-0.108019	-0.113702	-0.100390	-0.019162	-0.009460	-0.032172	-0.073960	-0.104822	-0.104623	-0.138606	-0.114311	-0.048430	-0.021202	-0.029842	-0.039300	-0.099465	-0.114491	-0.089632	-0.048977	0.006351	-0.006245	-0.040386	-0.039734	-0.054652	-0.020182	0.015605	0.018913	0.066169	0.038449	0.054226	0.054720	0.058437	0.125691	0.068844	0.065509	0.075470	0.122983	0.081596	0.047616	0.062208	-0.000165	-0.000650	-0.029704	-0.044236	-0.087628	-0.089603	-0.087017	-0.090294	-0.067751	-0.028043	-0.030697	-0.025587	-0.059362	-0.101363	-0.117981	-0.122267	-0.124671	-0.102644	-0.065314	0.003706	-0.036042	-0.083649	-0.147880	-0.150363	-0.143911	-0.130794	-0.063638	-0.029511	-0.039818	-0.103380	-0.144386	-0.167377	-0.172843	-0.112584	-0.038550	-0.005607	-0.087434	-0.101062	-0.097828	-0.029990	-0.039035	0.023570	0.026255	0.021826	0.054752	0.062647	0.080336	0.117244	0.128623	0.077520	0.145775	0.152921	0.126590	0.116395	0.097078	0.048103	-0.017660	-0.036749	-0.097847	-0.117422	-0.098018	-0.113466	-0.091345	-0.055727	-0.022075	-0.021101	-0.076965	-0.139468	-0.150641	-0.145440	-0.135304	-0.088194	-0.042512	-0.036364	-0.110371	-0.153957	-0.159603	-0.134937	-0.094672	-0.037775	-0.000966	-0.055404	-0.131611	-0.143946	-0.107091	-0.012385	0.058698	0.031366	-0.032517	-0.007254	0.013979	0.136667	0.126336	0.119633	0.096775	0.156131	0.157796	0.084150	0.170787	0.175374	0.129711	-0.021998	-0.068685	-0.114386	-0.076519	-0.106944	-0.066101	-0.043041	-0.025435	-0.028308	-0.097042	-0.145962	-0.101566	-0.140586	-0.087149	-0.040503	-0.031779	-0.102297	-0.148700	-0.160708	-0.090715	-0.036025	0.045020	0.034753	0.005658	0.017284	0.043416	0.096435	0.106903	0.100210	0.051454	0.156731	0.146312	0.163978	0.162779	0.156997	0.132233	-0.022777	-0.014738	-0.054486	-0.061434	-0.077282	-0.031951	-0.001709	-0.003894	-0.060225	-0.094764	-0.058905	-0.013130	-0.005318	0.040004	0.019934	-0.011226	0.029911	0.054925	0.118738	0.130590	0.091668	0.107770	0.147044	0.140892	0.151125	0.144838	0.149108	0.159178	-0.012497	0.006656	0.004614	-0.020188	-0.003823	-0.019803	0.033405	0.038591	0.055275	0.042308	0.062260	0.100696	0.131817	0.140644	0.080808	0.137208	0.159501	0.156455	0.146154	0.180608	0.101861	0.051901	0.046240	0.077191	0.030265	0.001946	0.084332	0.107810	0.112242	0.133409	0.131104	0.160394	0.154026	0.137104	0.171932	0.140321	0.026587	0.068706	0.102449	0.035980	0.101768	0.173463	0.140632	0.151472	0.177227	0.156678	0.067580	0.109405	0.062514	0.112936	0.164294	0.132865	0.054605	0.062229	1.000000	0.012632
A10T0G0C0	-0.001155	-0.004035	-0.007760	-0.019264	-0.023277	-0.023631	-0.021343	-0.019643	-0.007328	-0.001423	-0.000936	-0.012248	-0.012770	-0.018178	-0.024361	-0.034966	-0.036719	-0.033479	-0.019962	0.001963	-0.007927	-0.004201	-0.002036	-0.031468	-0.038471	-0.041720	-0.042954	-0.036642	-0.017468	-0.002962	-0.006538	-0.015579	-0.026604	-0.038341	-0.043743	-0.036811	-0.023338	-0.006992	0.000514	0.007053	-0.020637	-0.023670	-0.020748	-0.004130	0.000371	0.015991	0.019689	0.027023	0.020039	0.003899	0.012799	0.035960	0.046465	0.033893	0.037693	0.006005	0.030304	0.051155	0.042715	0.017886	0.034737	0.030699	0.023346	0.015654	0.006996	0.001765	-0.000705	-0.007045	-0.022246	-0.033409	-0.034551	-0.034522	-0.030107	-0.019446	-0.007233	-0.013669	-0.006953	-0.017613	-0.038682	-0.045316	-0.046251	-0.046575	-0.033319	-0.017487	-0.006687	-0.005209	-0.024053	-0.036169	-0.050552	-0.046399	-0.044478	-0.025036	-0.015457	0.006152	-0.020192	-0.035963	-0.040545	-0.035433	-0.009856	-0.004137	0.016734	0.010504	0.016915	0.010087	0.002379	-0.007012	0.040872	0.055325	0.047841	0.037816	0.025102	0.052360	0.048054	0.034340	0.024527	0.035101	0.040881	0.019419	0.028832	0.016192	0.018853	-0.005933	-0.020069	-0.035019	-0.040570	-0.041548	-0.040335	-0.027965	-0.013816	-0.004884	-0.010476	-0.027155	-0.043847	-0.048645	-0.051329	-0.040787	-0.024565	-0.009704	-0.019044	-0.018048	-0.040833	-0.039161	-0.042173	-0.028235	-0.005023	0.017509	0.007313	-0.008054	-0.018158	0.001678	0.007268	0.036755	0.040648	0.028829	0.012901	0.010523	0.034836	0.060293	0.048467	0.038136	0.036133	0.030047	0.008985	0.022459	0.028119	0.010563	-0.006483	-0.015487	-0.030750	-0.042371	-0.049483	-0.034132	-0.001640	-0.003366	-0.002508	-0.028320	-0.031991	-0.041047	-0.025723	-0.026581	0.000147	0.013751	-0.001044	-0.014446	-0.003505	-0.001437	-0.010809	0.024930	0.012724	0.028145	0.033355	0.024177	0.043500	0.049992	0.019690	-0.006962	0.025028	0.031533	0.025366	0.019201	0.013015	0.002225	-0.005104	-0.027924	-0.033857	-0.040204	-0.016761	-0.005121	-0.000473	0.005582	0.006373	-0.017802	-0.023503	-0.017583	0.013870	0.021110	0.032051	0.019816	0.008470	0.031567	0.027487	0.029405	0.006257	0.036960	0.018888	0.001641	0.021448	0.008527	0.016580	0.000032	-0.001503	0.012604	-0.026312	0.011753	0.010293	0.012216	0.024557	0.035466	0.041115	0.015223	0.054327	0.031206	0.051339	0.035756	0.005257	-0.006498	0.017670	0.031842	-0.014888	0.007909	-0.026907	0.013342	0.030934	0.033533	0.036161	0.018429	0.042502	0.033820	0.047017	0.033622	0.033842	0.036583	0.032471	0.014599	0.024182	0.002399	0.014851	0.028027	0.039600	0.038918	0.015779	0.037564	0.049641	0.025364	0.027247	0.010472	0.034703	0.029743	0.031101	0.032128	0.028655	0.024966	0.009158	0.028277	0.012632	1.000000

## Make a clustermap to verify the heatmap organized by the correlation among genes expression
cluster_map = sns.clustermap(corr, figsize = (10,14))

/home/ubuntu/.local/lib/python3.8/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/ubuntu/.local/lib/python3.8/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

Finding the most correlated genes in each species

Now we can find the oligomers that are more coorelated within a specific species, that can give us some insights in which oligmers have more activity or are acting together

for i in np.unique(Y):
    to_correlate = X[Y == i]

    cor = to_correlate.corr()

    c = cor.abs().unstack().drop_duplicates().reset_index()
    c = c.rename(columns={'level_0': 'Oligomer 1', 'level_1': 'Oligomer 2', 0: 'Correlation'})
    c = c.query('0.75 <= Correlation < 1').sort_values(by='Correlation', ascending=False).reset_index(drop=True)

    display(c.iloc[:10, :].style.background_gradient(cmap="flare")\
            .set_caption('Strongest oligomer correlation: {}'.format(label_encoder.inverse_transform([i])[0])))

Strongest oligomer correlation: Bacteroides_fragilis

	Oligomer 1	Oligomer 2	Correlation
0	A1T2G6C1	A1T2G7C0	0.870281
1	A3T4G3C0	A4T0G1C5	0.856541
2	A3T0G3C4	A3T1G3C3	0.822918
3	A5T0G2C3	A5T5G0C0	0.819760
4	A5T4G0C1	A5T5G0C0	0.809877
5	A3T0G3C4	A3T0G5C2	0.797221
6	A4T0G1C5	A5T0G2C3	0.790506
7	A4T0G1C5	A4T1G4C1	0.790386
8	A4T4G1C1	A5T0G2C3	0.787689
9	A4T3G2C1	A5T0G2C3	0.785406

Strongest oligomer correlation: Campylobacter_jejuni

	Oligomer 1	Oligomer 2	Correlation
0	A0T0G4C6	A0T0G6C4	0.994300
1	A0T0G3C7	A0T0G5C5	0.991872
2	A0T0G4C6	A1T0G5C4	0.990411
3	A0T0G3C7	A0T1G8C1	0.989749
4	A0T0G2C8	A0T0G7C3	0.989475
5	A0T0G5C5	A0T1G8C1	0.989145
6	A0T1G4C5	A1T0G5C4	0.987177
7	A0T1G3C6	A0T1G4C5	0.985951
8	A0T1G3C6	A1T0G5C4	0.985858
9	A0T0G5C5	A0T1G3C6	0.984055

Strongest oligomer correlation: Enterococcus_hirae

	Oligomer 1	Oligomer 2	Correlation
0	A0T0G5C5	A0T0G7C3	0.980968
1	A0T0G8C2	A1T0G0C9	0.970188
2	A0T0G5C5	A0T1G3C6	0.963262
3	A0T0G1C9	A0T0G5C5	0.958948
4	A0T0G6C4	A0T0G7C3	0.958417
5	A0T0G7C3	A0T1G3C6	0.954222
6	A0T0G4C6	A0T0G6C4	0.952755
7	A0T0G7C3	A0T1G7C2	0.951755
8	A0T0G5C5	A0T1G7C2	0.950764
9	A3T0G3C4	A3T0G5C2	0.944553

Strongest oligomer correlation: Escherichia_coli

	Oligomer 1	Oligomer 2	Correlation
0	A5T4G0C1	A5T5G0C0	0.904267
1	A5T3G0C2	A5T5G0C0	0.878857
2	A5T2G3C0	A5T5G0C0	0.862147
3	A4T4G1C1	A5T0G2C3	0.854780
4	A3T4G3C0	A4T1G4C1	0.854032
5	A5T0G2C3	A5T5G0C0	0.848432
6	A3T4G3C0	A4T0G1C5	0.829864
7	A3T4G3C0	A4T3G2C1	0.826269
8	A5T3G0C2	A5T4G0C1	0.824592
9	A4T4G1C1	A5T5G0C0	0.819823

Strongest oligomer correlation: Escherichia_fergusonii

	Oligomer 1	Oligomer 2	Correlation
0	A5T4G0C1	A5T5G0C0	0.916947
1	A1T0G0C9	A10T0G0C0	0.889570
2	A5T0G2C3	A5T5G0C0	0.863022
3	A5T3G0C2	A5T5G0C0	0.862381
4	A4T4G1C1	A5T0G2C3	0.859826
5	A5T2G3C0	A5T5G0C0	0.853642
6	A3T4G3C0	A4T1G4C1	0.848427
7	A0T10G0C0	A1T0G0C9	0.831520
8	A5T0G2C3	A5T4G0C1	0.820559
9	A5T3G0C2	A5T4G0C1	0.819497

Strongest oligomer correlation: Klebsiella_pneumoniae

	Oligomer 1	Oligomer 2	Correlation
0	A5T4G0C1	A5T5G0C0	0.956983
1	A5T3G0C2	A5T5G0C0	0.909700
2	A5T2G3C0	A5T5G0C0	0.901236
3	A4T4G1C1	A5T0G2C3	0.897347
4	A5T3G0C2	A5T4G0C1	0.895798
5	A3T4G3C0	A4T0G1C5	0.887094
6	A5T2G3C0	A5T4G0C1	0.886851
7	A3T4G3C0	A4T1G4C1	0.883673
8	A5T0G2C3	A5T5G0C0	0.870512
9	A4T3G2C1	A4T4G1C1	0.865305

Strongest oligomer correlation: Salmonella_enterica

	Oligomer 1	Oligomer 2	Correlation
0	A5T4G0C1	A5T5G0C0	0.916334
1	A4T4G1C1	A5T0G2C3	0.883507
2	A3T4G3C0	A4T0G1C5	0.880559
3	A5T3G0C2	A5T5G0C0	0.876061
4	A5T0G2C3	A5T5G0C0	0.850378
5	A5T2G3C0	A5T5G0C0	0.849775
6	A4T0G1C5	A4T1G4C1	0.840070
7	A5T3G0C2	A5T4G0C1	0.838710
8	A3T4G3C0	A4T1G4C1	0.832868
9	A5T2G3C0	A5T4G0C1	0.823300

Strongest oligomer correlation: Staphylococcus_aureus

	Oligomer 1	Oligomer 2	Correlation
0	A0T0G3C7	A0T1G1C8	0.991636
1	A0T0G8C2	A1T0G0C9	0.985847
2	A1T0G5C4	A1T1G8C0	0.982484
3	A1T0G3C6	A1T0G5C4	0.979763
4	A0T0G7C3	A0T1G1C8	0.979002
5	A0T0G6C4	A0T1G6C3	0.977503
6	A0T1G1C8	A1T0G3C6	0.976998
7	A1T0G3C6	A1T1G8C0	0.976606
8	A0T1G6C3	A1T0G5C4	0.976515
9	A0T1G6C3	A1T0G3C6	0.976507

Strongest oligomer correlation: Streptococcus_pneumoniae

	Oligomer 1	Oligomer 2	Correlation
0	A0T0G8C2	A1T0G0C9	0.995343
1	A0T0G2C8	A0T0G7C3	0.990913
2	A0T0G5C5	A0T1G8C1	0.983138
3	A0T0G5C5	A0T0G7C3	0.980339
4	A0T0G7C3	A1T0G2C7	0.975143
5	A0T0G2C8	A0T0G5C5	0.974446
6	A0T0G6C4	A1T0G2C7	0.972514
7	A0T0G5C5	A1T0G2C7	0.970136
8	A0T0G2C8	A1T0G2C7	0.966608
9	A0T0G5C5	A0T1G3C6	0.966330

Strongest oligomer correlation: Streptococcus_pyogenes

	Oligomer 1	Oligomer 2	Correlation
0	A0T0G5C5	A0T0G7C3	0.981113
1	A0T0G5C5	A0T1G1C8	0.980433
2	A0T0G7C3	A0T1G1C8	0.972056
3	A0T0G6C4	A1T0G5C4	0.963176
4	A0T0G4C6	A0T0G6C4	0.960012
5	A0T0G6C4	A0T0G7C3	0.959965
6	A0T0G6C4	A0T1G6C3	0.957442
7	A0T0G7C3	A0T1G6C3	0.953419
8	A0T1G1C8	A1T0G5C4	0.953183
9	A0T0G7C3	A1T0G5C4	0.949832

## Verifying the composition of the two more correlated 
## oligomers in the dataset (A0T0G4C6 and A0T0C6G4). 
def oligomer_frequency( oligomer ):
    '''Insert the oligomer in a string format to obtain a graph of
    its spectrum.'''
    sns.set_context("notebook", font_scale=1.5)
    sns.set_style("white")
    sns.set_color_codes(palette='deep')
    f, ax = plt.subplots(figsize=(8, 6))
    sns.barplot(x= oligomer, y = "target", palette= 'flare',
                hue = "target", data= dfd, legend = False)
    plt.tick_params(axis='x', labelsize=15, rotation=-45)
    plt.tick_params(axis='y', labelsize=15, rotation=0)
    plt.xlabel("Oligomer spectrum")
    plt.ylabel("Bacterial Species")

oligomer_frequency("A0T0G4C6")
oligomer_frequency("A0T0G6C4")

oligomer_frequency("A1T2G6C1")

oligomer_frequency("A0T10G0C0")

3. Reducing the columns of the data

The PCA is a very important method of decomposotion and reduction of dimensionality, it can be what help us to visualize the data and give us some insights.

PCA

X_mean = np.mean(X, axis=0)
 
X_std = np.std(X, axis=0)
 
# Standardization
Z = (X - X_mean) / X_std
Z.head()

	A0T0G0C10	A0T0G1C9	A0T0G2C8	A0T0G3C7	A0T0G4C6	A0T0G5C5	A0T0G6C4	A0T0G7C3	A0T0G8C2	A0T0G9C1	...	A8T0G0C2	A8T0G1C1	A8T0G2C0	A8T1G0C1	A8T1G1C0	A8T2G0C0	A9T0G0C1	A9T0G1C0	A9T1G0C0	A10T0G0C0
row_id
0	-0.018954	-0.106202	-0.169948	-0.371265	-0.443894	-0.434489	-0.389575	-0.382683	-0.217309	-0.029434	...	-0.358090	-0.541363	-0.449209	-0.585178	-0.574842	-0.464978	-0.214879	-0.243653	-0.234931	-0.090106
1	-0.018954	-0.106202	-0.169948	2.704190	-0.443894	1.049061	-0.389575	-0.382683	-0.217309	-0.029434	...	-0.358090	-0.541363	-0.449209	0.534586	0.322804	-0.464978	-0.214879	-0.243653	-0.234931	-0.090106
2	-0.018954	0.014716	0.099803	0.376070	0.465373	0.322122	0.368270	0.381667	0.286130	0.012766	...	-0.182765	-0.328810	-0.309082	-0.398178	-0.402494	-0.333424	-0.150629	-0.198682	-0.173987	0.045525
3	-0.003650	-0.045743	0.126778	0.732823	0.900577	0.695976	0.630009	0.611931	0.240363	0.012766	...	-0.129136	-0.245039	-0.229010	-0.287321	-0.316320	-0.240764	-0.115292	-0.181193	-0.117245	-0.090106
4	-0.018954	-0.106202	-0.169948	-0.371265	-0.443894	-0.434489	-0.389575	-0.382683	-0.217309	-0.029434	...	-0.358090	-0.541363	-0.449209	-0.585178	-0.574842	-0.464978	-0.214879	-0.243653	-0.234931	-0.090106

5 rows × 286 columns

c = Z.cov()
eigenvalues, eigenvectors = np.linalg.eig(c)

print('Eigen values:\n', eigenvalues)
print('Eigen values Shape:', eigenvalues.shape)
print('Eigen Vector Shape:', eigenvectors.shape)

Eigen values:
 [5.83948451e+01 2.08184421e+01 9.81317552e+00 5.88179776e+00
 4.91080440e+00 3.01303197e+00 2.25830138e+00 1.95212584e+00
 1.67557301e+00 1.63017644e+00 1.50067936e+00 1.43827354e+00
 1.35265529e+00 2.45976738e-16 1.30318370e+00 1.29055311e+00
 1.26514309e+00 1.23751991e+00 1.22976531e+00 1.22557500e+00
 1.21562906e+00 1.20506935e+00 1.19200644e+00 1.18259941e+00
 1.17945500e+00 1.17104565e+00 1.16372740e+00 1.15093651e+00
 1.14222862e+00 1.14512201e+00 1.13293273e+00 1.12400907e+00
 1.11792970e+00 1.11188500e+00 1.10371759e+00 1.10005189e+00
 1.06058429e-01 1.09730258e-01 1.15586786e-01 1.21034781e-01
 1.26046645e-01 1.36737380e-01 1.39337554e-01 1.50003301e-01
 1.53931860e-01 1.60374126e-01 1.09273819e+00 1.08678454e+00
 1.08253514e+00 1.07555128e+00 1.07203643e+00 1.06826868e+00
 1.06553690e+00 1.05557170e+00 1.05023658e+00 1.03423459e+00
 1.03823772e+00 1.04265577e+00 1.63181521e-01 1.68905172e-01
 1.70399520e-01 1.73807325e-01 1.82968194e-01 1.03163996e+00
 1.02650644e+00 1.02180216e+00 1.01858293e+00 1.01107825e+00
 9.99908034e-01 9.96987291e-01 1.89639270e-01 1.92459204e-01
 1.99081820e-01 2.03580423e-01 2.07676146e-01 2.12337493e-01
 9.92351730e-01 9.90743577e-01 9.84809292e-01 9.79714679e-01
 9.75905514e-01 9.73256897e-01 9.63546650e-01 9.67067418e-01
 2.17345626e-01 2.21627850e-01 2.26367742e-01 2.29534583e-01
 2.35606241e-01 2.41756591e-01 2.42574262e-01 9.57832729e-01
 9.52117971e-01 9.55429435e-01 9.49319808e-01 8.98293849e-01
 9.40459323e-01 9.06492596e-01 9.34095675e-01 9.35302881e-01
 9.10461843e-01 9.15773580e-01 9.26366522e-01 9.24478659e-01
 9.20026226e-01 2.48430122e-01 2.53260909e-01 8.94301591e-01
 2.58423614e-01 2.60685468e-01 2.62303621e-01 8.88642027e-01
 8.84457727e-01 8.81617145e-01 8.78461266e-01 2.64853628e-01
 2.68404814e-01 2.71286850e-01 2.74831929e-01 8.70711766e-01
 8.68071089e-01 8.68950493e-01 8.64128775e-01 8.54935555e-01
 8.57864802e-01 8.41970336e-01 8.45419408e-01 8.45711259e-01
 2.75757628e-01 2.82564169e-01 2.80527874e-01 2.87549393e-01
 2.91490972e-01 2.89668550e-01 8.33320849e-01 8.31817861e-01
 8.23067126e-01 8.15051331e-01 8.06905732e-01 8.12045796e-01
 8.11096508e-01 8.00981425e-01 2.93388917e-01 2.95623042e-01
 3.00122578e-01 3.25756237e-01 3.15450825e-01 3.21077060e-01
 3.19487967e-01 3.02555390e-01 3.03930580e-01 3.04880379e-01
 3.08764837e-01 3.10043333e-01 7.97460824e-01 7.94547369e-01
 7.89143484e-01 3.32835844e-01 3.28980394e-01 3.36685359e-01
 7.84723732e-01 7.87009338e-01 7.73289379e-01 7.74215331e-01
 7.70134930e-01 3.40164889e-01 3.38066318e-01 3.45462517e-01
 3.48044118e-01 3.52925004e-01 3.54179541e-01 3.56166582e-01
 3.59429399e-01 3.63325086e-01 3.79968790e-01 3.67765301e-01
 3.70577541e-01 3.73757744e-01 7.64689053e-01 7.58124846e-01
 7.33357269e-01 7.39039016e-01 7.40245881e-01 7.54201697e-01
 7.51781729e-01 7.48160291e-01 7.49405554e-01 7.28679104e-01
 3.81631049e-01 3.82579051e-01 3.87478729e-01 7.24114082e-01
 7.18788559e-01 7.26351650e-01 7.08269139e-01 7.07627921e-01
 3.91097034e-01 3.96340837e-01 3.98689563e-01 3.99022436e-01
 7.11711555e-01 7.12434929e-01 6.97799046e-01 6.94674288e-01
 6.92264352e-01 6.86376392e-01 6.83937607e-01 6.78236980e-01
 6.74430284e-01 4.49208588e-01 4.43115202e-01 4.39470830e-01
 4.32072858e-01 4.35471621e-01 4.13722704e-01 4.25058663e-01
 4.20568435e-01 4.22029225e-01 4.16783969e-01 4.06913852e-01
 4.04382630e-01 4.09019120e-01 4.51148004e-01 4.56919737e-01
 4.36818552e-01 6.64329505e-01 6.42777981e-01 6.47332634e-01
 4.84868631e-01 4.81275591e-01 4.78337535e-01 4.75665491e-01
 4.64439830e-01 4.66243378e-01 4.99847927e-01 6.07388493e-01
 6.14410550e-01 6.16931283e-01 6.61950782e-01 6.37804324e-01
 6.36392607e-01 6.53977299e-01 6.50084430e-01 4.71740057e-01
 5.06264900e-01 4.97028104e-01 6.00729727e-01 6.03296330e-01
 6.24601574e-01 6.32133780e-01 6.55407913e-01 6.62658246e-01
 5.08655699e-01 5.17238134e-01 5.36646316e-01 5.38248884e-01
 5.96280685e-01 6.25461248e-01 4.59085413e-01 6.68581744e-01
 5.15264113e-01 5.29981170e-01 5.42948558e-01 5.84934875e-01
 5.87303966e-01 4.70632210e-01 6.28044464e-01 5.77795347e-01
 5.66190482e-01 5.80438482e-01 5.26175899e-01 5.28200903e-01
 5.23353737e-01 5.58471343e-01 5.46082567e-01 5.56755884e-01
 4.91399654e-01 5.52240613e-01 5.10661742e-01 5.92064570e-01
 5.91783488e-01 5.74593783e-01 5.70005612e-01 5.52520021e-01
 4.92780769e-01 5.57415144e-01]
Eigen values Shape: (286,)
Eigen Vector Shape: (286, 286)

# Index the eigenvalues in descending order 
idx = eigenvalues.argsort()[::-1]
 
# Sort the eigenvalues in descending order 
eigenvalues = eigenvalues[idx]
 
# sort the corresponding eigenvectors accordingly
eigenvectors = eigenvectors[:,idx]

pca_data = np.dot( eigenvectors.transpose() , Z.transpose() ).transpose()
print(pca_data)

[[-3.43491119e-01  2.18814135e+01  1.53372891e+00 ... -5.46751331e-01
  -1.05818981e-01  8.88178420e-16]
 [-6.01955049e+00  2.18581374e+00  3.38777896e-01 ...  3.36461855e-01
   4.18764118e-02  7.32747196e-15]
 [-6.77554183e+00 -1.12525564e+00  1.32909984e-01 ... -1.52883494e-02
   6.15723236e-03 -1.77635684e-14]
 ...
 [ 1.10172823e-01 -1.42179957e+00  2.55961529e+00 ... -1.72003978e-02
   1.43489068e-02  5.91887650e-15]
 [-7.14465935e+00 -1.26184516e+00 -7.73459158e-01 ... -3.99118286e-03
  -7.03799847e-02 -1.11022302e-14]
 [-2.84316217e+00 -9.69228121e-01  2.95793647e+00 ... -1.29743732e-02
   7.80082339e-03 -1.58761893e-14]]

This interactive graphic can help us to visualize the distribution of the data and the proximity among different classes. For instance, in this projection we can see proximity between the classes of Campylobacter jejuni and Streptococcus pneumoniae, and a great distance between Campylobacter jejuni and Escherichia coli.

from sklearn.decomposition import PCA

pca     = PCA(n_components=10).fit_transform(Z)
pca_df  = pd.DataFrame(data=pca, columns=['PC'+str(i+1) for i in range(0,10)]).reset_index(drop=True)
species = dfd['target'].reset_index(drop=True).str.replace('_', ' ') 
pca_df  = pd.concat([species, pca_df], axis=1)
pca_df['map'] = pca_df['target'].map(pca_df['target'].value_counts())
pca_df = pca_df.sort_values(by='map', ascending=False).drop('map', axis=1)

pal = sns.color_palette("plasma", 12).as_hex()[:10]
fig = px.scatter(pca_df, x='PC1', y='PC2', color='target', color_discrete_sequence=pal, opacity=0.4)

fig.show()

Feature selection

Here's some analysis we have to do to select features, because our dataset is a way to big. Moreover, even with a good prediction using these variables the problem is that we have to get too much information to actually predict the kind of bacteria we have.

gene_features  = df.iloc[:,:-1]
target_species = df.iloc[:,-1:]

print(gene_features.shape)
print(target_species.shape)

(200000, 286)
(200000, 1)

# Initialize LabelEncoders
gene_feature_encoder = LabelEncoder()
target_species_encoder = LabelEncoder()

# Convert DataFrame columns to 1-dimensional arrays
gene_features_flat = gene_features.values.flatten()
target_species_flat = target_species.values.flatten()

# Encode the features and target species
gene_features_encoded = gene_feature_encoder.fit_transform(gene_features_flat)
target_species_encoded = target_species_encoder.fit_transform(target_species_flat)

# Reshape gene_features_encoded to original shape
gene_features_encoded = gene_features_encoded.reshape(gene_features.shape)

# Assuming gene_features_encoded.shape[1] gives the number of features
column_names = [f'feature_{i}' for i in range(gene_features_encoded.shape[1])]  # Access number of features
new_df = pd.DataFrame(gene_features_encoded, columns=column_names)
new_df['target'] = target_species_encoded

print("Original Dataset:") 
print(df.head()) 

Original Dataset:
           A0T0G0C10  A0T0G1C9  A0T0G2C8  A0T0G3C7  A0T0G4C6  A0T0G5C5  \
row_id                                                                   
0      -9.536743e-07 -0.000010 -0.000043 -0.000114 -0.000200 -0.000240   
1      -9.536743e-07 -0.000010 -0.000043  0.000886 -0.000200  0.000760   
2      -9.536743e-07 -0.000002  0.000007  0.000129  0.000268  0.000270   
3       4.632568e-08 -0.000006  0.000012  0.000245  0.000492  0.000522   
4      -9.536743e-07 -0.000010 -0.000043 -0.000114 -0.000200 -0.000240   

        A0T0G6C4  A0T0G7C3  A0T0G8C2  A0T0G9C1  ...  A8T0G1C1  A8T0G2C0  \
row_id                                          ...                       
0      -0.000200 -0.000114 -0.000043 -0.000010  ... -0.000086 -0.000043   
1      -0.000200 -0.000114 -0.000043 -0.000010  ... -0.000086 -0.000043   
2       0.000243  0.000125  0.000001 -0.000007  ...  0.000084  0.000048   
3       0.000396  0.000197 -0.000003 -0.000007  ...  0.000151  0.000100   
4      -0.000200 -0.000114 -0.000043 -0.000010  ... -0.000086 -0.000043   

        A8T1G0C1  A8T1G1C0  A8T2G0C0  A9T0G0C1  A9T0G1C0  A9T1G0C0  \
row_id                                                               
0      -0.000086 -0.000086 -0.000043 -0.000010 -0.000010 -0.000010   
1       0.000914  0.000914 -0.000043 -0.000010 -0.000010 -0.000010   
2       0.000081  0.000106  0.000072  0.000010  0.000008  0.000019   
3       0.000180  0.000202  0.000153  0.000021  0.000015  0.000046   
4      -0.000086 -0.000086 -0.000043 -0.000010 -0.000010 -0.000010   

           A10T0G0C0                  target  
row_id                                        
0      -9.536743e-07  Streptococcus_pyogenes  
1      -9.536743e-07     Salmonella_enterica  
2       1.046326e-06     Salmonella_enterica  
3      -9.536743e-07     Salmonella_enterica  
4      -9.536743e-07      Enterococcus_hirae  

[5 rows x 287 columns]

from sklearn.feature_selection import SelectKBest, chi2

k = 100
chi2_selector = SelectKBest(chi2, k=k) 
X_new = chi2_selector.fit_transform(gene_features_encoded, target_species_encoded)

selected_features = df.columns[:-1][chi2_selector.get_support()] 
print("\nSelected Features:") 
print(selected_features)

Selected Features:
Index(['A0T1G4C5', 'A0T1G5C4', 'A0T2G3C5', 'A0T2G4C4', 'A0T2G5C3', 'A0T3G3C4',
       'A0T3G4C3', 'A0T4G3C3', 'A1T0G4C5', 'A1T0G5C4', 'A1T1G2C6', 'A1T1G3C5',
       'A1T1G4C4', 'A1T1G5C3', 'A1T1G6C2', 'A1T2G2C5', 'A1T2G3C4', 'A1T2G4C3',
       'A1T2G5C2', 'A1T3G2C4', 'A1T3G3C3', 'A1T3G4C2', 'A1T4G2C3', 'A1T4G3C2',
       'A1T6G1C2', 'A1T6G2C1', 'A1T7G1C1', 'A2T0G3C5', 'A2T0G4C4', 'A2T0G5C3',
       'A2T1G2C5', 'A2T1G3C4', 'A2T1G4C3', 'A2T1G5C2', 'A2T2G2C4', 'A2T2G3C3',
       'A2T2G4C2', 'A2T3G2C3', 'A2T3G3C2', 'A2T4G1C3', 'A2T4G2C2', 'A2T4G3C1',
       'A2T5G0C3', 'A2T5G1C2', 'A2T5G2C1', 'A2T5G3C0', 'A2T6G0C2', 'A2T6G1C1',
       'A2T6G2C0', 'A2T7G0C1', 'A3T0G3C4', 'A3T0G4C3', 'A3T1G2C4', 'A3T1G3C3',
       'A3T1G4C2', 'A3T2G2C3', 'A3T2G3C2', 'A3T3G1C3', 'A3T3G2C2', 'A3T3G3C1',
       'A3T4G0C3', 'A3T4G1C2', 'A3T4G2C1', 'A3T4G3C0', 'A3T5G0C2', 'A3T5G1C1',
       'A3T5G2C0', 'A3T6G0C1', 'A3T6G1C0', 'A4T1G2C3', 'A4T2G1C3', 'A4T2G2C2',
       'A4T2G3C1', 'A4T3G0C3', 'A4T3G1C2', 'A4T3G2C1', 'A4T3G3C0', 'A4T4G0C2',
       'A4T4G1C1', 'A4T4G2C0', 'A4T5G0C1', 'A4T5G1C0', 'A4T6G0C0', 'A5T2G0C3',
       'A5T2G1C2', 'A5T2G2C1', 'A5T3G0C2', 'A5T3G1C1', 'A5T3G2C0', 'A5T4G0C1',
       'A5T4G1C0', 'A6T1G1C2', 'A6T1G2C1', 'A6T2G0C2', 'A6T2G1C1', 'A6T2G2C0',
       'A6T3G0C1', 'A6T3G1C0', 'A7T1G1C1', 'A7T2G1C0'],
      dtype='object')

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X_new, target_species_encoded, test_size=0.2, random_state=42)

print("Shape of X_train_selected:", X_train.shape)
print("Shape of X_test_selected:", X_test.shape)

Shape of X_train_selected: (160000, 100)
Shape of X_test_selected: (40000, 100)

4. Models of classification

Here we are going to test different models of machine learning to do our classification, logistic regression, Random Forest, Decision Trees and etc.

Logistic regression

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

model = LogisticRegression(max_iter = 500, n_jobs = -1)  
model.fit(X_train, y_train)

predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print("Accuracy:", accuracy)

Accuracy: 0.734825

from sklearn.model_selection import KFold 
from sklearn.model_selection import cross_val_score

num_folds = 5
kf = KFold(n_splits=num_folds, shuffle=True, random_state=42)

cross_val_results1 = cross_val_score(model ,X_new, target_species_encoded, cv=kf, n_jobs = -1)

print(cross_val_results1)

[0.735    0.733425 0.735625 0.732275 0.734925]

RandomForest

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42, n_jobs = -1)
rf.fit(X_train, y_train)

rf1_predictions = rf.predict(X_test)

accuracy_rf = accuracy_score(y_test, rf1_predictions)
print("Accuracy:", accuracy_rf)

Accuracy: 0.66015

from skopt.space import Integer

params = {
    'n_estimators': Integer(10, 500),
    'max_depth': Integer(1, 50),
    'min_samples_split': Integer(2, 20),
    'min_samples_leaf': Integer(1, 20)
}

from skopt import BayesSearchCV

opt = BayesSearchCV(rf, params, n_iter=32, cv=3, n_jobs=-1, random_state=42)
opt.fit(X_train, y_train)

print("Best parameters found: ", opt.best_params_)

y_pred = opt.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: ", accuracy)

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[33], line 4
      1 from skopt import BayesSearchCV
      3 opt = BayesSearchCV(rf, params, n_iter=32, cv=3, n_jobs=-1, random_state=42)
----> 4 opt.fit(X_train, y_train)
      6 print("Best parameters found: ", opt.best_params_)
      8 y_pred = opt.predict(X_test)

File ~/.local/lib/python3.8/site-packages/skopt/searchcv.py:542, in BayesSearchCV.fit(self, X, y, groups, callback, **fit_params)
    535 if callable(self.refit):
    536     raise ValueError(
    537         "BayesSearchCV doesn't support a callable refit, "
    538         "as it doesn't define an implicit score to "
    539         "optimize"
    540     )
--> 542 super().fit(X=X, y=y, groups=groups, **fit_params)
    544 # BaseSearchCV never ranked train scores,
    545 # but apparently we used to ship this (back-compat)
    546 if self.return_train_score:

File ~/.local/lib/python3.8/site-packages/sklearn/base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~/.local/lib/python3.8/site-packages/skopt/searchcv.py:599, in BayesSearchCV._run_search(self, evaluate_candidates)
    595 while n_iter > 0:
    596     # when n_iter < n_points points left for evaluation
    597     n_points_adjusted = min(n_iter, n_points)
--> 599     optim_result, score_name = self._step(
    600         search_space,
    601         optimizer,
    602         score_name,
    603         evaluate_candidates,
    604         n_points=n_points_adjusted,
    605     )
    606     n_iter -= n_points
    608     if eval_callbacks(callbacks, optim_result):

File ~/.local/lib/python3.8/site-packages/skopt/searchcv.py:453, in BayesSearchCV._step(self, search_space, optimizer, score_name, evaluate_candidates, n_points)
    450 # make lists into dictionaries
    451 params_dict = [point_asdict(search_space, p) for p in params]
--> 453 all_results = evaluate_candidates(params_dict)
    455 # if self.scoring is a callable, we have to wait until here
    456 # to get the score name
    457 if score_name is None:

File ~/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~/.local/lib/python3.8/site-packages/sklearn/utils/parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~/.local/lib/python3.8/site-packages/joblib/parallel.py:2007, in Parallel.__call__(self, iterable)
   2001 # The first item from the output is blank, but it makes the interpreter
   2002 # progress until it enters the Try/Except block of the generator and
   2003 # reaches the first `yield` statement. This starts the asynchronous
   2004 # dispatch of the tasks to the workers.
   2005 next(output)
-> 2007 return output if self.return_generator else list(output)

File ~/.local/lib/python3.8/site-packages/joblib/parallel.py:1650, in Parallel._get_outputs(self, iterator, pre_dispatch)
   1647     yield
   1649     with self._backend.retrieval_context():
-> 1650         yield from self._retrieve()
   1652 except GeneratorExit:
   1653     # The generator has been garbage collected before being fully
   1654     # consumed. This aborts the remaining tasks if possible and warn
   1655     # the user if necessary.
   1656     self._exception = True

File ~/.local/lib/python3.8/site-packages/joblib/parallel.py:1762, in Parallel._retrieve(self)
   1757 # If the next job is not ready for retrieval yet, we just wait for
   1758 # async callbacks to progress.
   1759 if ((len(self._jobs) == 0) or
   1760     (self._jobs[0].get_status(
   1761         timeout=self.timeout) == TASK_PENDING)):
-> 1762     time.sleep(0.01)
   1763     continue
   1765 # We need to be careful: the job list can be filling up as
   1766 # we empty it and Python list are not thread-safe by
   1767 # default hence the use of the lock

KeyboardInterrupt: 

from sklearn.metrics import precision_recall_fscore_support

precision, recall, f1_score, _ = precision_recall_fscore_support(y_test, rf1_predictions ,average='weighted')

print("Precision:", precision)
print("Recall:", recall)
print("F1-Score:", f1_score)

Precision: 0.679507066968466
Recall: 0.66015
F1-Score: 0.6654905941301749

from sklearn.metrics import classification_report, confusion_matrix

cm = confusion_matrix(y_test, rf1_predictions)
print(cm)

[[2780   38  260   93  103    1   22   45  294  383]
 [ 492 2797  197    0    2    0    0  243   76  227]
 [ 283  117 2206    9    9    0    0  131  682  598]
 [  75    1   49 2701  557  124  246    0   62  163]
 [ 140    0   43  807 2564   62  150    3   71  146]
 [  17    0   12  169   97 3463   98    2   21   45]
 [  39    0   46  581  250  309 2669    6   44  100]
 [ 233  312  160    2    1    0    0 2554  325  389]
 [ 260   73  442   17    4    0    0   70 2497  663]
 [ 152   62  400   16   11    0    0  139 1023 2175]]

plt.figure(figsize = (10,8))
sns.heatmap(cm,
            annot=True,
            fmt='g'
            )
plt.ylabel('y_predicted', fontsize=13)
plt.xlabel('y_test', fontsize=13)
plt.title('Confusion Matrix', fontsize=17)
plt.show()

num_folds = 5
kf = KFold(n_splits=num_folds, shuffle=True, random_state=42)

cross_val_results2 = cross_val_score(rf,X_new, target_species_encoded, cv=kf, n_jobs = -1)

print(cross_val_results2)

[0.661525 0.6661   0.66165  0.660875 0.660125]

Decision_Tree

from sklearn.tree import DecisionTreeClassifier

clfs = DecisionTreeClassifier()
clfs.fit(X_train, y_train)

dt_pres=clfs.predict(X_test)
dt_val_acc=accuracy_score(y_test, dt_pres)

dt_val_acc

0.973125

num_folds = 5
kf = KFold(n_splits=num_folds, shuffle=True, random_state=42)

cross_val_results3 = cross_val_score(clfs,X_new, target_species_encoded, cv=kf, n_jobs= -1)

print(cross_val_results3)

[0.9733  0.97495 0.9744  0.97505 0.97475]

from sklearn.metrics import classification_report, confusion_matrix

cm_clsf = confusion_matrix(y_test, dt_pres)
print(cm_clsf)

[[3936    3   15   14   11    0    8    4   16   12]
 [   3 3973   11    0    1    0    2   29    4   11]
 [  15   14 3920    3    2    0    0   13   20   48]
 [   4    0    6 3813   93    5   42    0    6    9]
 [   5    0    2  100 3837    8   29    1    0    4]
 [   1    0    0   14    5 3867   32    0    5    0]
 [   8    2    0   40   25   25 3935    0    5    4]
 [   3   26   11    0    2    1    2 3900   16   15]
 [  16    5   19    2    1    1    3   13 3904   62]
 [  14   15   31    1    0    0    3   11   63 3840]]

plt.figure(figsize = (10,8))
sns.heatmap(cm_clsf,
            annot=True,
            fmt='g'
            )
plt.ylabel('y_predicted', fontsize=13)
plt.xlabel('y_test', fontsize=13)
plt.title('Confusion Matrix', fontsize=17)
plt.show()

In that we saw that the simpler model was the more efficient, in what the metrics indicate the best model is the Decision Tree, achieving more accurate classifications.

Separating variables using RandomForestClassifier criterion of importance

Now we are going to make a small study using other way to separate the most important variables, using the RandomForestClassifier and verifying how much of the metrics we will lose.

from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report

def selected_features(X,Y,p):
    model = RandomForestClassifier(n_estimators=200, random_state=42, n_jobs= -1)
    model.fit(X, Y)
    
    feature_importances = model.feature_importances_
    
    importance_df = pd.DataFrame({
        'Feature': X.columns,
        'Importance': feature_importances
    }).sort_values(by='Importance', ascending=False)
    
    cumulative_importance = importance_df['Importance'].cumsum()
    selected_features = importance_df[cumulative_importance <= p]['Feature'].values
    
    return X[selected_features] , selected_features

def XGB_model(X,Y,p):
    X_selected, _ = selected_features(X,Y,p)
    X_train, X_test, y_train, y_test = train_test_split(X_selected, Y, test_size=0.3, random_state=42)

    modelXGB = XGBClassifier(n_jobs=-1)  
    modelXGB.fit(X_train, y_train)
    
    y_pred_XGB = modelXGB.predict(X_test)
    
    return accuracy_score(y_test, y_pred_XGB), classification_report(y_test, y_pred_XGB, output_dict = True)

acc, report = XGB_model(X,Y,0.25)
print(acc)
print(pd.DataFrame(report))

0.9111511371579117
                     0            1            2            3            4  \
precision     0.933634     0.916579     0.887595     0.918926     0.899063   
recall        0.926990     0.940081     0.878081     0.895631     0.915735   
f1-score      0.930300     0.928181     0.882813     0.907129     0.907322   
support    3794.000000  3705.000000  3732.000000  3708.000000  3667.000000   

                     5            6            7            8            9  \
precision     0.957231     0.938974     0.904991     0.870669     0.884822   
recall        0.954933     0.911416     0.917727     0.888289     0.882211   
f1-score      0.956081     0.924990     0.911314     0.879391     0.883515   
support    3750.000000  3714.000000  3695.000000  3706.000000  3727.000000   

           accuracy     macro avg  weighted avg  
precision  0.911151      0.911248      0.911345  
recall     0.911151      0.911109      0.911151  
f1-score   0.911151      0.911104      0.911173  
support    0.911151  37198.000000  37198.000000  

_, features_025 = selected_features(X,Y,0.25)
corr_025        = corr.loc[features_025, features_025]
cluster_map_025 = sns.clustermap(corr_025)

thresholds = [1, 0.75, 0.6, 0.4, 0.3, 0.2, 0.1]
accuracies = []
reports = []

for threshold in thresholds:
    acc, report = XGB_model(X,Y, threshold)
    accuracies.append(acc)
    reports.append(report)

def plot_metric(metric_name, ylabel):
    plt.figure(figsize = (8,7))
    for number in range(10):
        metric_values = [report[str(number)][metric_name] for report in reports]
        plt.plot(thresholds, metric_values, label=number, alpha=0.8)
    plt.axhline(y=0.95, color='r', linestyle='--', alpha=0.8)
    plt.xlabel('Thresholds')
    plt.ylabel(ylabel)
    plt.legend()
    plt.grid(True)
    plt.show()

plot_metric('precision', 'Precision')
plot_metric('recall', 'Recall')
plot_metric('f1-score', 'f1-score')

In these graphs we can see that the threshold to start to lose our scores is between 0.8 and 0.6, what reduce our dataset to only 75% of the variables we had before. So the feature selection using this method is not so efficient as using the chi2selector.

5. Conclusion

After analyzing the data, we successfully reduced the dataset size by lowering the number of variables while maintaining model performance. The best results were achieved with a Decision Tree, using the Chi2Selector and SelectKBest methods for feature selection and dimensionality reduction.